This document presents a detailed description of the challenge on clarifying questions for dialogue systems (ClariQ). The challenge is organized as part of the Conversational AI challenge series (ConvAI3) at Search Oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In IR settings such a situation is handled mainly thought the diversification of the search result page. It is however much more challenging in dialogue settings with limited bandwidth. Therefore, in this challenge, we provide a common evaluation framework to evaluate mixed-initiative conversations. Participants are asked to rank clarifying questions in an information-seeking conversations. The challenge is organized in two stages where in Stage 1 we evaluate the submissions in an offline setting and single-turn conversations. Top participants of Stage 1 get the chance to have their model tested by human annotators.
翻译:本文件详细描述了在澄清对话系统问题方面的挑战(ClariQ),在2020年的 " 探索性对话AI(SCAI)EMNLP " 研讨会上,作为 " 交流性对话挑战系列 " (ConvAI3)的一部分组织起来。对话系统的主要目的是根据用户的要求回复适当的答案。然而,一些用户的要求可能含糊不清。在IR 环境中,处理这种情况时主要想到搜索结果页面的多样化。然而,在带宽有限的对话环境中,这种情形更具挑战性。因此,我们为评价混合性对话提供了一个共同的评估框架。要求参与者在信息搜索对话中排列澄清问题的位置。在第一阶段,我们分两个阶段评估提交材料的离线设置和单点对话。第1阶段的参与者有机会让人类警告者测试其模型。